骨髓细胞形态学对于识别恶性血液病是至关重要的。基于卷积神经网络的骨髓细胞形态自动分类模型在诊断效率和准确性方面显示出了相当大的潜力。然而，由于骨髓细胞分类算法的准确性不够令人满意，自动分类骨髓细胞在临床设施中的使用频率较低。为了解决这一问题，在本文中，我们提出了一种名为双注意力门的DenseNet（DAGDNet）来构建一种新颖、高效和高精度的骨髓细胞分类模型，以进一步提高分类模型的性能。DAGDNet是通过在DenseNet的架构中嵌入一种新颖的双注意力门机制来构建的。双注意力门用于过滤和突出显示DenseNet中与位置相关的特征，以提高基于神经网络的细胞分类器的准确性和召回率。我们构建了一个来自重庆医科大学第一附属医院的骨髓细胞形态数据集，主要包括白血病样本，用于训练和测试我们提出的DAGDNet以及骨髓细胞分类数据集。在多中心数据集上评估时，实验结果表明我们提出的DAGDNet在骨髓细胞分类性能方面优于DenseNet和ResNeXt等图像分类模型。在慕尼黑白血病实验室数据集上，DAGDNet的平均精确度为88.1%，达到了最先进的性能水平，同时仍保持高效。我们的数据表明，DAGDNet可以提高自动骨髓细胞分类的效果，并可以作为临床应用中的辅助诊断工具。此外，DAGDNet也是一种高效的模型，可以快速检查大量的骨髓细胞，并可以降低误诊的概率。© 2023作者（们），在Springer Nature集团旗下德国Springer-Verlag GmbH公司的独家许可下。

The morphology of bone marrow cells is essential in identifying malignant hematological disorders. The automatic classification model of bone marrow cell morphology based on convolutional neural networks shows considerable promise in terms of diagnostic efficiency and accuracy. However, due to the lack of acceptable accuracy in bone marrow cell classification algorithms, automatic classification of bone marrow cells is now infrequently used in clinical facilities. To address the issue of precision, in this paper, we propose a Dual Attention Gates DenseNet (DAGDNet) to construct a novel efficient, and high-precision bone marrow cell classification model for enhancing the classification model's performance even further.DAGDNet is constructed by embedding a novel dual attention gates (DAGs) mechanism in the architecture of DenseNet. DAGs are used to filter and highlight the position-related features in DenseNet to improve the precision and recall of neural network-based cell classifiers. We have constructed a dataset of bone marrow cell morphology from the First Affiliated Hospital of Chongqing Medical University, which mainly consists of leukemia samples, to train and test our proposed DAGDNet together with the bone marrow cell classification dataset.When evaluated on a multi-center dataset, experimental results show that our proposed DAGDNet outperforms image classification models such as DenseNet and ResNeXt in bone marrow cell classification performance. The mean precision of DAGDNet on the Munich Leukemia Laboratory dataset is 88.1%, achieving state-of-the-art performance while still maintaining high efficiency.Our data demonstrate that the DAGDNet can improve the efficacy of automatic bone marrow cell classification and can be exploited as an assisting diagnosis tool in clinical applications. Moreover, the DAGDNet is also an efficient model that can swiftly inspect a large number of bone marrow cells and offers the benefit of reducing the probability of an incorrect diagnosis.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.